PM2.5 forecasting is an important scientific way to control environmental pollution and keep people healthy. To achieve high-performance PM2.5 forecasting, a new ensemble reinforcement learning gated unit model is presented in this study. The complete framework of this model mainly includes the following steps: In step I, the WPD method is applied to decompose PM2.5 data into 8 sub-series with different frequency types. In step II, the SAE-GRU method is presented to finish the establishment of sub-series forecasting model. Among them, SAE is used to obtain low-latitude features of PM2.5 data, and GRU is applied to finish PM2.5 sub-series forecasting. In step III, Q-learning is used to combine the every PM2.5 sub-series to get the final model prediction results. By comparing and analyzing the final results of all case study, it can be summarized that (1) Q-learning-based ensemble model integrates the subseries with different frequency types perfectly, and results prove that it is better than heuristic algorithm, and (2) the proposed ensemble reinforcement learning gated unit model can get prediction results beyond seventeen alternative models which include three most state-of-the-art models in all cases.

中文翻译：

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一种用于每日 PM2.5 预测的新型集成强化学习门控单元模型

PM2.5预报是控制环境污染、保持人们健康的重要科学手段。为了实现高性能 PM2.5 预测，本研究提出了一种新的集成强化学习门控单元模型。该模型的完整框架主要包括以下步骤： 在步骤I中，应用WPD方法将PM2.5数据分解为8个不同频率类型的子序列。第二步，提出SAE-GRU方法，完成子序列预测模型的建立。其中，SAE用于获取PM2.5数据的低纬度特征，GRU用于完成PM2.5子序列预测。在第三步中，Q-learning 用于组合每个 PM2.5 子序列，得到最终的模型预测结果。通过比较和分析所有案例研究的最终结果，